DCIS Tissue Marker Profiling for Recurrence Risk Stratification
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Solution Overview
Problem
Current methods for predicting and treating ductal carcinoma in situ (DCIS) recurrence and invasive breast cancer are inadequate, leading to overtreatment and unnecessary side effects due to the inability to accurately assess recurrence risk, particularly for lumpectomy-eligible patients.
Innovation Solution
A method involving the analysis of DCIS tissue samples for markers such as PR, HER2, SIAH2, and FOXA1 to predict the risk of subsequent ipsilateral breast events, allowing for tailored treatment approaches that can include more aggressive or less aggressive therapies based on marker profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to predict and treat DCIS recurrence and invasive breast cancer, then treatment is provided to all patients, but overtreatment and unnecessary side effects occur due to inability to accurately assess recurrence risk
Solution Approach 1:
The patent segments patients into distinct risk groups (low-risk and high-risk) based on marker expression profiles. By analyzing multiple markers simultaneously and clustering patients based on their marker patterns, the invention divides the homogeneous treatment approach into heterogeneous risk-stratified groups, enabling precise identification of who needs aggressive treatment and who can avoid it.
Solution Approach 2:
The patent changes the parameters used for risk assessment from traditional single-marker or clinical-only approaches to a multi-marker expression profile analysis. By measuring and comparing expression levels of multiple markers (such as ER, PR, HER2, Ki67, and other prognostic markers) and using these expression patterns as the basis for risk classification, the invention achieves more accurate risk stratification.
2Reliability
If aggressive therapy is applied to all DCIS patients, then recurrence risk is reduced for high-risk patients, but low-risk patients undergo unnecessary surgeries and side effects
Solution Approach 1:
The patent performs preliminary risk assessment using marker expression analysis before treatment decisions are made. By evaluating the expression profiles of multiple markers in the DCIS tissue samples obtained during diagnosis, the invention determines the patient's risk category in advance, allowing treatment plans to be customized before surgery or adjuvant therapy begins, rather than applying uniform aggressive treatment to all patients.
Solution Approach 2:
The patent applies different treatment qualities to different patient subgroups based on their local (individual) marker expression characteristics. Instead of a one-size-fits-all approach, the invention tailors the intensity and type of treatment to each patient's specific risk profile determined by their marker pattern, providing localized (personalized) treatment quality matched to their actual recurrence risk.
3Measurement precision
If traditional single-marker or clinical factors are used for risk assessment, then treatment decisions are simple, but accuracy in predicting DCIS recurrence and invasive cancer is insufficient
Solution Approach 1:
The patent merges multiple marker analyses into a unified risk assessment framework. By combining the expression data from multiple markers (ER, PR, HER2, Ki67, and other prognostic markers) and integrating them through clustering algorithms, the invention creates a comprehensive risk prediction model that leverages the complementary information from each marker to achieve higher accuracy than any single marker alone.
Solution Approach 2:
The patent uses computational clustering algorithms to create virtual groupings of patients based on their marker expression patterns. Instead of manually evaluating each marker and making complex decisions, the invention uses algorithms to automatically copy and compare expression profiles across patients, identifying patterns and assigning risk categories through computational analysis that simplifies the decision-making process while maintaining high accuracy.
Data Source
AI summary
The present technology generally relates to methods and compositions relevant to the prediction that a subject with and/or after treatment for DCIS will experience a subsequent ipsilateral breast event that is a DCIS recurrence, an invasive breast cancer, both a DCIS recurrence and invasive cancer, or neither. The technology can assist one with how to treat such subjects.